Abstract
Hallux valgus is a common feet problem. A hallux valgus deformity is when there is medial deviation of the first metatarsal and lateral deviation of the great toe. In this work, we introduce an algorithm for automatic recognition of hallux valgus on X-ray images with feet. The bones are segmented on the basis of U-Net convolutional neural network. The neural network has been trained on thirty manually segmented images by an orthopedist. We present both qualitative and quantitative segmentation results on ten test images. We present algorithms for great toe extraction and hallux valgus angle (HVA) estimation. The HVA is estimated as the angle between two lines fitted to big toe skeleton. We compare results that were obtained manually, by computer-assisted programs that are used by radiologists, and by the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dechter, R.: Learning while searching in constraint-satisfaction-problems. In: Proceedings of the Fifth AAAI National Conference on Artificial Intelligence, pp. 178–183. AAAI Press (1986)
Aizenberg, I., Aizenberg, N., Vandewalle, J.: Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Kluwer Academic Publishers, Norwell (2000)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015)
Fogel, A., Kvedar, J.: Artificial intelligence powers digital medicine. NPJ Digit. Med. 1(1), 5 (2018)
Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019)
Mayo, R.C., Leung, J.: Artificial intelligence and deep learning - radiology’s Next frontier? Clin. Imaging 49, 87–88 (2018)
Fazal, M.I., Patel, M.E., Tye, J., Gupta, Y.: The past, present and future role of artificial intelligence in imaging. Eur. J. Radiol. 105, 246–250 (2018)
Liew, C.: The future of radiology augmented with artificial intelligence: a strategy for success. Eur. J. Radiol. 102, 152–156 (2018)
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. 8(1) (2018)
Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-Rays with deep learning. CoRR abs/1711.05225 (2017)
Islam, J., Zhang, Y.: Towards robust lung segmentation in chest radiographs with deep learning. CoRR abs/1811.12638 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8, 1 (2018)
Wu, J., Mahfouz, M.R.: Robust X-ray image segmentation by spectral clustering and active shape model. J. Med. Imaging 3 (2016)
Stolojescu-Crisan, C., Stefan, H.: An interactive X-ray image segmentation technique for bone extraction. In: International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 1164–1171 (2014)
Mohammadi, H.M., de Guise, J.A.: Enhanced X-ray image segmentation method using prior shape. IET Comput. Vision 11(2), 145–152 (2017)
Liszka, H., Gądek, A.: Results of scarf osteotomy without implant fixation in the treatment of hallux valgus. Foot Ankle Int. 39(11), 1320–1327 (2018)
Dinato, M., de Faria Freitas, M., Milano, C., Valloto, E., Ninomiya, A.F., Pagnano, R.G.: Reliability of two smartphone applications for radiographic measurements of hallux valgus angles. J. Foot Ankle Surg. 56(2), 230–233 (2017)
Srivastava, S., Chockalingam, N., Fakhri, T.E.: Radiographic measurements of hallux angles: a review of current techniques. Foot 20(1), 27–31 (2010)
Heineman, N., Chhabra, A., Zhang, L., Dessouky, R., Wukich, D.: Point vs. traditional method evaluation of hallux valgus: interreader reliability and intermethod performance using X-ray and MRI. Skeletal Radiol. 48(2), 251–257 (2019)
Yang, W., et al.: Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med. Image Anal. 35, 421–433 (2017)
Olczak, J., et al.: Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 88(6), 581–586 (2017)
Wülker, N., Mittag, F.: The treatment of hallux valgus. Deutsches Ärzteblatt Int. 109(49), 857–868 (2012)
Garrow, A.P., Papageorgiou, A., Silman, A.J., Thomas, E., Jayson, M.I.V., Macfarlane, G.J.: The grading of hallux valgus. The Manchester scale. J. Am. Podiatr. Med. Assoc. 91(2), 74–78 (2001)
Lee, K.M., Ahn, S., Chung, C.Y., Sung, K., Park, M.: Reliability and relationship of radiographic measurements in hallux valgus. Clin. Orthop. Relat. Res. 470(9), 2613–2621 (2012)
Schneider, W., Csepan, R., Knahr, K.: Reproducibility of the radiographic metatarsophalangeal angle in hallux surgery. J. Bone Joint Surg. Am. 85–A, 494–499 (2003)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-05088-0
RadiAnt: Radiant DICOM Viewer
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kwolek, K., Liszka, H., Kwolek, B., Gądek, A. (2019). Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-Ray Images. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-30493-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30492-8
Online ISBN: 978-3-030-30493-5
eBook Packages: Computer ScienceComputer Science (R0)